10,000 Matching Annotations
  1. Mar 2026
    1. Reviewer #3 (Public review):

      Summary:

      The authors studied the organisation of orientation and direction-selective retinal ganglion cells' boutons in the mouse superior colliculus. They confirmed the results already published (Molotkov, 2023; de Malmazet, 2024; Vita, 2024; Laniado, 2025), that retinal ganglion cells' boutons in the superior colliculus conserve the retinal organisation. Thereby, orientation and direction preferences of retinal boutons at each collicular location reflect the tuning of retinal ganglion cells found at the corresponding retinal location, that is covering a matching receptive field location.

      The authors also studied the organization of orientation and direction-selective neurons in the superior colliculus. They report a lack of functional organisation in the superior colliculus for neurons preferring the same stimulus orientation or direction of movement. This goes against several published reports (Ahmadlou and Heimel, 2015; Liang et al., 2023; De Malmazet et al., 2018; Feinberg and Meister, 2014; Kasai and Isa, 2021; Li et al., 2020) but echoes a study from Chen et al. (Chen, 2021). The latter authors contested the strength of the anatomical clustering of tuned alike direction-selective neurons. They found, however, that in about a quarter of their recordings, direction-selective cells with similar preferred directions did cluster anatomically in the superior colliculus.

      Here, the authors of the current manuscript under review report that local clustering of tuning was weak in all neural populations and confined to very small spatial scales (10-20 μm). This is one order of magnitude smaller than previously reported clusters of around 100-300μm wide. Therefore, the authors conclude that orientation and direction tuning in the mouse superior colliculus follows a salt and pepper organisation.

      Strengths & Weaknesses:

      Although the authors performed a solid analysis contesting the functional clustering of direction and orientation selective neurons, there seemed to be some elements in their data in favour of a functional clustering of neurons.

      As an illustration, the authors plotted in Figure 1Q the distribution of preferred orientations from all their recorded orientation-selective cells. The curve shows a clear bias, indicating that neurons preferring horizontal orientations were found two times more often than neurons encoding any other orientations. Moreover, the authors recorded all their neurons from a defined anatomical location of the colliculus, marked by the dotted rectangle in Figure 3A-C. Therefore, this suggests that orientation-selective cells in this particular collicular location are biased towards preferring horizontal orientations. This supports an anatomical clustering of tuned alike orientation-selective cells in the superior colliculus.

      Similarly, Figure 1P shows a bias in the preferred directions of direction-selective neurons in the same recording area. Cells tended to respond more to upward and forward-moving stimuli. The bias is more modest than the one described above for preferred orientations. However, it still seems significant. For example, cells preferring upwards movements appeared to be four times more abundant than cells preferring downward movements. As a consequence, it indicates that preferred directions might not be uniformly distributed and equally represented across the superior colliculus.

      These anatomical biases are also visible in the receptive field analysis of the paper. In Figure 3G, the authors plotted the distribution of preferred orientations for every 10-degree bins within the recorded field of view. Out of 26 bins containing more than one neuron, only 6 seemed to include cells not overwhelmingly preferring a single orientation. These were located towards the top right of the figure. Therefore, over almost 80% of the recorded superior colliculus, the data seem in agreement with the view that orientation-selective cells tend to prefer the same orientation at a given receptive location.

      The patch analysis in Figures 4G and H also seems to show some degree of coherence in the preferred orientation and direction of neighbouring tuned collicular cells. In both Figures 4 G and H, clear patches of similar preferred orientation and direction appeared to emerge. For example, in Figure 4H, there is a predominance of horizontally tuned patches. This was expected given the recording bias consisting of a majority of horizontally tuned cells. In addition, vertical and 45-degree patches are also visible, in blue and red, respectively. These patches overlap with the corresponding retinotopic locations in Figure 3G, where the histograms show that cells tend to prefer the same orientations, horizontal, vertical or 45 degrees.

      It is important to note that in the previous studies on functional clustering of orientation and direction, variability in the tuning of cells within clusters was always reported (Ahmadlou and Heimel, 2015; Chen et al., 2021; De Malmazet et al., 2018; Feinberg and Meister, 2014; Kasai and Isa, 2021; Li et al., 2020). This was more marked for direction-selective cells than for orientation-selective cells. In general, cells preferring all four cardinal directions were often recorded at any given collicular location. Similarly, orientation-selective cells could be found to prefer deviant orientations compared to adjacent cells. Therefore, it is not surprising to see locally mixed tuning in collicular neurons. However, what appeared significant in these studies was the overall proportion of cells with similar tuning in patches of the superior colliculus. As described above, this also seems to be the case in the data of this manuscript.

      To conclude, it seems that authors tend to overlook the sources of agreement between their data and previous reports showing functional clustering of cells in the superior colliculus. Instead, the authors tend to emphasise the dissimilarities and variability to put forward a contentious view on the organisation of orientation and direction selectivity in neurons of the superior colliculus. This, I fear, is detrimental to the field because it creates a sort of manufactured chaos that produces unnecessary confusion for readers who do not attentively read the manuscript. It would be valuable for the authors to consider rewriting the manuscript, acknowledging where their data, in fact, support some level of functional clustering.

    2. Author Response:

      We thank the reviewers and editors for their thoughtful and constructive assessment. We are encouraged that the reviewers viewed the combination of retinal bouton imaging, collicular neuron imaging, and depth-resolved electrophysiology, together with the comparison to retinal geometric models, as a strength of the study. As the reviewers note, our findings are consistent with previous in vitro studies showing topographic organization of tuning in the retina and with recent work demonstrating the precision of retinotopic mapping from retina to superior colliculus (SC). In revision, we will refine our definition of tuning, sharpen our claims about the spatial organization across SC and its correspondence to retinal topography, and make clearer our aim of reconciling seemingly opposing findings in the literature. In addition, we will provide a detailed response to all other points raised by the reviewers.

      A central point raised in the reviews concerns our definition of direction- and orientation-selective cells. We agree that relying only on statistical significance is not sufficient for our purposes, because the resulting classification can depend on recording duration and statistical power. In the revised manuscript, we will therefore introduce thresholding criteria for direction and orientation selectivity indices (DSI and OSI) in addition to significance-based testing. We will also make clearer that our primary question is which stimulus directions and orientations are represented at a given receptive field location, rather than the distribution of preferences among neurons classified as purely direction- or orientation-selective.

      We will also revise the text to define more precisely what our data do and do not establish about the large-scale organization across SC. Our intended conclusion is not that we identify a novel global organization, which would require sampling a larger portion of visual space, but rather that the regions we sampled are not well explained by previously proposed global maps in which each visual field location is dominated by a single tuning preference and the same organization is conserved across individuals. Instead, our data are more consistent with a retinal organization of biases toward specific directions and orientations that vary systematically across visual space.

      We will further clarify how we quantified the correspondence between our data and the previously established retinal model of direction and orientation tuning. In the current manuscript, we report the errors between model predictions and measured tuning preferences at the corresponding visual field locations. We then assess model performance by comparing the distribution of these errors with the errors obtained from two surrogate datasets: one in which the correspondence between visual field location and tuning preference is destroyed, and one in which the prior distribution of tuning preferences is assumed to be uniform. In the revised manuscript, we will make the interpretation of this comparison more explicit, so that the reported errors are clearly presented as the relevant effect-size measure alongside significance.

      Finally, we appreciate the reviewers’ concern that the manuscript may currently emphasize disagreement with previous studies too strongly. We will revise the Discussion to better acknowledge where our data support some degree of local bias or weak clustering, while clarifying that we do not find evidence for a robust, stereotyped global map that is consistent across animals. Our goal is to sharpen the manuscript so that it better reconciles seemingly divergent findings in the literature rather than setting them in opposition.

    1. I myself kept burning out in a structured corporate setting but now thrive in a flexible research one

      Fun being in a corporate-like research environment. Or rather a research environment where you are not involved with any research yourself. :/

    1. eLife Assessment

      This important study advances our understanding of the neural substrate of planning trajectories towards a goal by using recurrent neural networks. The manuscript provides solid evidence for most of the claims, but it remains unclear whether the dynamics do indeed bear the defining characteristics of attractors, and the interpretation and scope of some claims may need to be reassessed in light of prior work. The work will be of broad interest to theoretical and systems neuroscientists and to cognitive scientists.

    2. Reviewer #1 (Public review):

      Summary:

      This work builds a theory to implement planning trajectories towards a goal in a known environment, inspired by analyses of prefrontal neural recordings. Unlike standard neural architectures for this task, such as value-based learning and successor representations, their proposed theory is able to adapt to novel goal locations within a trial. The key to the theory is that future times are represented by orthogonal groups of neurons. The recurrent connectivity between groups of neurons selective to specific future times and locations reflects the learned knowledge of the task. Finally, the authors show that standard networks trained on the task approximate their proposed theory.

      Strengths:

      The structure of the work is clear, and the presentation of the results is very well written, which is particularly noticeable given the consequential amount of results presented. The authors are able to link their theory with experimental findings in neural recordings. The reverse-engineering of trained recurrent neural networks is very thorough, by analyzing both dynamics and connectivity. The assumptions and predictions of their model are clearly stated.

      Weaknesses:

      It is unclear whether their proposed theory, "space-time attractors", actually is an attractor network. The authors used recurrent neural networks with very few timesteps, and long single neuron time constants with respect to the task time scales. Attractor networks, as the ones the authors cite, refer to networks that generate nontrivial patterns of activity through recurrent interactions, after long periods of time.

      The authors gloss over how the reward inputs are calculated. Computing these reward inputs should be part of the planning process, and the authors are implicitly leaving this problem aside. How does the reward input, which includes future time and location, depend on the actions that have not yet been taken by the agent? It feels like most of the planning computation is already provided by these reward inputs at the beginning of the trial. It could be that the network is only learning to process the planned sequence of actions present in the inputs.

    3. Reviewer #2 (Public review):

      This well-written manuscript proposes to use attractors in space and time (STA) as a mechanistic explanation for planning in the prefrontal cortex. The main conceptual hypothesis is that planning is implemented as attractor dynamics in a representation that encodes states at each time step jointly. Depending on inputs, the network relaxes to a trajectory that already contains future states that will be visited at each time step, rather than computing a scalar value at each point in time and space like other classical approaches from RL. The authors compare this approach to implementations such as TD learning and successor representation, and further show that trained recurrent neural networks on specific tasks involving planning develop structured subspaces resembling the ones postulated in STA.

      The idea of treating attracting trajectories unfolding in time as the computational substrate for planning is very interesting and potentially important. The explicit construction of a state x time representational space and its implementation via recurrent dynamics are appealing and convincing in the idealized tasks considered. I found the manuscript to be refreshingly explicit regarding several of the assumptions and limitations of the models, for example, the fact that certain advantages can be viewed as properties of the state space itself and not necessarily of a fundamentally new planning mechanism.

      Overall, the manuscript presents a cool attractor model that extends in time and explores its performance in a subset of illustrative tasks involving planning. My doubts concern mostly the interpretation and scope of the claims made in the manuscript. Here are a few comments where I detail my questions/concerns:

      (1) The authors nicely discuss that much of the difference between STA and classical TD or SR agents is "in some sense a property of the state space rather than the decision making algorithm," and that TD and SR could in principle be implemented in a comparable space x time representation. This is fair, but it also suggests that the central contribution of the manuscript lies primarily in the representational factorization (state x time tiling) and its dynamical implementation via attractors, rather than in a fundamentally new planning algorithm or theory, mechanistic or not. I think theory should be distinguished from mechanism, and it would therefore help the reader to describe the conceptual advancement more as a novel mechanism or implementation than a novel (mechanistic) theory for decision/planning.

      (2) Related to my previous point, I think it would be helpful to position STA more explicitly relative to computational/theoretical literature in which attractor networks encode temporally ordered patterns (so effectively including future times). For example, classical extensions of Hopfield networks with asymmetric connectivity implement retrieval of sequences and ordered transitions between patterns (Sompolinsky & Kanter, 1986). More recently, sequential attractors and limit-cycle dynamics have been constructed in structured recurrent networks by the Morrison group (Parmelee et al., 2021). These works do not implement an explicit discretized state x future-time tiling as in STA and do not specifically discuss the usage for planning. However, they do provide concrete precedents for attractor dynamics over temporally structured trajectories in terms of mechanism. It would be useful to discuss this literature and clarify a little what's new mechanistically in the view of the authors.

      (3) A central claim of the manuscript is that space-time trajectories are attractors of the STA dynamics. The manuscript does provide empirical evidence consistent with attractor-like behavior. However, it is not explicitly shown whether trajectory representations persist in the absence of sustained external inputs. So it's not clear to me whether the trajectories should be interpreted as intrinsic attractors of the recurrent system, which can be selected by delivering transient inputs, or whether they must be stabilized by a specific continuous external drive. It would be useful if the author could clarify/discuss this point.

      As far as I understand it, reward information is provided as input to specific populations encoding future time steps, and that's essential for rapid adaptation without rewiring connectivity. How such future-time-specific reward inputs would be generated and routed to distinct neural populations isn't entirely clear to me. Since this seems to be an essential component of the model, I think it would be important to discuss more deeply the source and plausibility of these reward signals related to different timesteps.

      (4) The authors note that vanilla STA scales linearly with planning horizon, and discuss potentially hierarchical extensions for longer horizons. They acknowledge that learning abstractions remains an open challenge, yet the examples of planning in the manuscript are restricted to very short temporal horizons and limited branching complexity. It is not obvious to me in what cases the current implementation and interpretation of STA remains viable (for example, in terms of relaxation iterations) as the horizon and branching factor increase. Relatively simple planning can be managed by simpler, less costly models/algorithms, whereas complex planning is a lot harder to deal with, and it's something that a mechanistic "theory" should address. In the context of the claims of the paper in its present form, I think this is possibly the most important conceptual and practical limitation in the manuscript.

      (5) The RNN analyses show that trained networks develop structured subspaces aligned with future time indices and exhibit perturbation behavior consistent with attractor-like dynamics. The manuscript also explicitly notes differences between the trained RNN and the handcrafted STA (e.g., long-range couplings between subspaces and differences in behavior of lower-value trajectories under perturbation), which I much appreciated. My doubt is on the specificity of this result, as trained RNNs on fixed-horizon tasks can develop latent dimensions correlated with temporal progress within a trial or time-to-goal. I think it would help the reader to clarify whether the results demonstrate that STA-like computations emerge in RNNs trained on planning tasks, or that RNNs generally develop some kind of structured spacetime representations when tasks involve future timesteps and some degree of flexibility in the decisions.

      A few more minor points, mainly concerning clarity:

      (1) The main dynamical equation combines a log-domain recurrent term, a floor operation, and a log-sum-exp normalization step, followed by exponentiation. The intuition/logic behind this specific formulation could be clarified for the reader. For example tt would be helpful to explain why the recurrent input appears inside a log, and also whether/how these operations relate to any multiplicative constraint.

      (2) While the computational cost of successor representation in an expanded NT x NT representation is discussed, the corresponding scaling of STA in terms of number of units and connections (as a function, for example, of the planning horizon) isn't clear to me. Perhaps the authors could compare costs more explicitly.

      (3) In the RNN analyses, structured subspaces aligned with future time indices are shown. I couldn't find a quantification of how much variance is captured by the subspaces, relative to other latent dimensions. Adding it would help get a feeling for the strength of the alignment.

    1. eLife Assessment

      This important study presents evidence that the Chromatin-linked adaptor for MSL complex proteins (CLAMP) GA-binding transcription factor (TF) regulates ~75% of HS-induced repression in Drosophila and suggests that CLAMP is the first known transcription factor to induce heat-stress-mediated repression of gene expression. While mechanistic details remain to be sorted out, this manuscript provides convincing evidence that novel pathways involving the CLAMP transcription factor repress gene expression during heat shock stress.

    2. Reviewer #1 (Public review):

      Summary:

      This work aims to identify the transcription factor responsible for targeting constitutively active genes for repression during heat stress. While the mechanisms underlying heat-stress-induced gene activation are well characterized - primarily involving Heat Shock Factor (HSF), the GA-binding factor GAF, and RNA Polymerase II pausing regulators - far less is known about how repression of constitutive genes is directed. Because known activation factors such as HSF and GAF do not account for repression, the authors sought a DNA-binding factor that could selectively target these genes. They focused on CLAMP (Chromatin-linked adaptor for MSL complex proteins) for several reasons. First, CLAMP recognizes GA-rich DNA motifs similar to those bound by GAF, suggesting it could compete with GAF at regulatory elements and shift transcriptional outcomes. Second, CLAMP has been shown to antagonize GAF binding in certain genomic contexts, suggesting it could counteract activation mechanisms. Third, CLAMP interacts with Negative Elongation Factor (NELF), a factor known to regulate transcriptional repression during heat stress. Finally, CLAMP promotes long-range chromatin interactions, indicating it may influence local chromatin architecture during the heat-stress response. Together, these properties led the authors to hypothesize that CLAMP helps mediate heat-stress-induced transcriptional repression of constitutively active genes.

      To test this hypothesis, the authors use immunofluorescence along with three techniques: (1) nascent RNA-sequencing (SLAM-seq) to define the function of CLAMP in heat stress-induced transcriptional activation and repression; (2) Micro-C to identify CLAMP-dependent and independent genome-wide, high-resolution local changes in chromatin organization after heat stress, and (3) HiChIP to identify CLAMP-bound 3D chromatin loop anchors associated with heat-stress-dependent transcriptional regulation.

      Analysis of heat-shocked salivary glands or KC cells showed results that aligned across all experiments, indicating that CLAMP is the primary repressor of gene activation upon heat shock. CLAMP also inhibits chromatin loop formation.

      Strengths:

      The techniques used here are comprehensive, and impressively, the data is unambiguous.

      Weaknesses:

      These techniques do not reveal the molecular mechanisms, but the authors provide a strong rationale and molecular hypotheses for future studies to dissect.

    3. Reviewer #2 (Public review):

      In this manuscript, Aguilera et al. investigate the mechanisms underlying transcriptional repression of constitutively expressed genes during heat stress. While the activation of heat-shock genes has been extensively studied, the mechanisms responsible for widespread transcriptional repression remain poorly understood. The authors propose that the GA-binding transcription factor CLAMP acts as a major regulator of heat-stress-induced transcriptional repression in Drosophila. Using nascent RNA-sequencing approaches, they report that CLAMP contributes to the repression of a large fraction of genes whose transcription decreases upon heat stress. In addition, the authors generate high-resolution Micro-C datasets to analyze changes in chromatin architecture during heat stress and report widespread alterations in chromatin looping associated with transcriptional changes. Based on these results, the study proposes that CLAMP regulates repression through both direct transcriptional mechanisms and modulation of local 3D genome architecture.

      The study addresses an important question in gene regulation: how transcription is rapidly repressed during environmental stress. The work is timely because most previous studies have focused on transcriptional activation of heat-shock genes, whereas repression mechanisms remain comparatively less understood. The integration of transcriptional profiling with high-resolution chromatin conformation data is a major strength of the manuscript and provides a valuable resource for the community studying genome organization and stress responses.

      The nascent RNA-sequencing experiments appear carefully designed and allow the authors to capture rapid transcriptional responses to heat stress. These data provide convincing evidence that heat stress leads to widespread transcriptional repression of constitutive genes and that CLAMP contributes substantially to this process. The genomic analyses linking CLAMP binding to repressed genes are also informative and support the idea that CLAMP plays a direct regulatory role at many loci.

      Another strength of the study is the generation of Micro-C datasets under heat stress conditions. These datasets provide a high-resolution view of chromatin architecture and reveal dynamic changes in local chromatin looping associated with transcriptional responses. The authors' analysis suggests that heat stress induces widespread reorganization of chromatin contacts, and that CLAMP may contribute to these structural changes. This dataset is likely to be useful for future studies exploring how environmental cues influence genome organization.

      Despite these strengths, several aspects of the study would benefit from further clarification. First, the mechanism by which CLAMP mediates transcriptional repression remains insufficiently defined. While the data support a role for CLAMP in the repression of a subset of genes during heat stress, the molecular basis of this effect is not fully explored. Second, although the Micro-C dataset represents a valuable resource for studying chromatin architecture during heat stress, the functional interpretation of the observed structural changes could be further developed. In particular, it would be helpful to better establish the relationship between the identified chromatin loops and gene regulation, and to clarify whether these structural changes play a causal role in transcriptional repression or instead reflect broader chromatin reorganization associated with the stress response.

    4. Reviewer #3 (Public review):

      Summary:

      Exposure to heat shock results in major changes to gene expression programs within the cell, and over the past decades, there has been extensive characterization of the mechanisms through which heat shock activates transcription. However, heat shock also leads to widespread repression of many genes, and the transcriptional mechanisms that mediate this repression have not been well understood. Here, the authors show that the transcription factor CLAMP mediates this heat shock-dependent repression via changes in local 3D chromatin looping. Intriguingly, CLAMP is already bound to chromatin prior to heat shock, but is necessary for the loss of local chromatin loops at its bound sites and repression of genes located within the loops. This study is significant because it defines chromatin looping, depending on a key transcription factor CLAMP, as the major mechanism through which genome-wide changes in gene repression occur in response to an inducible stimulus, heat shock.

      Strengths:

      The use of the SLAM-seq and Micro-C techniques to measure the necessity of CLAMP for heat shock-dependent transcription repression and local chromatin looping is excellent, and these approaches provide valuable insight into the role of CLAMP in heat shock-dependent repression that was not apparent with older approaches. The HiChIP approach provides an excellent method to test whether CLAMP is bound at sites where there are changes in looping upon heat shock, providing good support for their conclusions that CLAMP induces heat shock repression by decreasing loops. Appropriate controls are present, and there is robust statistical analysis of the bioinformatics data.

      Weaknesses:

      The study does not provide insight into how CLAMP mechanistically affects loops upon heat shock, although the discussion raises the possibility that this could result from biophysical changes since CLAMP is an intrinsically disordered protein.

    1. The results of the moderator analysis are displayed in Tables 6 and 7. For both horizontally and vertically orientated outcome moderators, differences between subgroups were non-significant with low to moderate heterogeneity. For horizontally orientated outcomes, the effect size for programme duration, as measured by number of weeks, was larger for those studies longer than 7 weeks (0.96 [− 0.15, 2.08]) than it was for those shorter than 7 weeks (0.43 [− 0.00, 0.86]) and favoured HPT in both cases. For vertically orientated outcomes, there was little difference between longer (> 8 weeks; − 0.19 [− 0.83, 0.46]) and shorter programmes (≤ 8 weeks; 0.08 [− 0.28, 0.44]. For horizontally orientated outcomes, there was a large effect size (1.91 [0.87, 2.96]) for programmes that had more than twelve sessions, with only a small effect size (0.30 [− 0.06, 0.66]) in those that had fewer than twelve. Again, this favoured HPT in both cases. The trend was not apparent for

      Training intensity and time frame favored HPT or horizontal plyometrical trainiing.

    2. If this is the case, a transition by coaches to more horizontally orientated exercises and outcome measures is warranted, though not at the complete expense of vertically orientated.

      Originally showed VPT was trialed individually but HPT needs to be individually analyzed and accessed which helps with with future research or training regimens.

  2. drive.google.com drive.google.com
    1. !,. have tried to turn myselfAmerican-ferri'inine. Chinese communication was loud, pub-lic. Only sick people had to whisper.

      This part is troubling because it shows that the narrator feels pressure to change herself to fit into American culture. She believes that her original way of speaking is not acceptable, which may lead to a loss of her identity.

    2. !,. have tried to turn myselfAmerican-ferri'inine. Chinese communication was loud, pub-lic. Only sick people had to whisper.

      This part is interesting because it shows the difference between Chinese and American communication styles. In Chinese culture, speaking loudly is normal, but in American culture, it may be seen as rude. This highlights the cultural conflict the narrator experiences.

    1. who were part of that first year exchange between Fieldston and University Heights could answer this question. Does it hurt or help the public school kids?

      This part is interesting because it asks whether the exchange program helps or hurts public school students, which shows the difference between schools.

    1. ust because we use an ethics framework to look at a situation doesn’t mean that we will come out with a morally good conclusion.

      I was most interested in the part saying ethics frameworks do not guarantee moral goodness. I agree because people can use the same framework to defend very different actions. This reminded me that ethical thinking in technology is not just about picking one theory, but about staying critical, comparing perspectives, and asking who might be harmed by a decision.

    2. Focuses on responsibilities and relational issues in the relationships you are invested in.

      I want to add to ethics of care. The reading says it focuses on responsibilities in relationships, but I think it is also useful for social media because it highlights emotional harm that rule-based frameworks may miss. For example, even if a platform follows the same rule for everyone, it may still fail vulnerable users who need more protection and support.

    3. 2.2.3. Ethics Frameworks

      Confucianism reminded me a lot of my own culture and experience. My family emphasizes a lot on the idea of respect for elderlys and maintaining peace in relationships between friends and family. I think this quality helped me to develop patience, but it's also harder to express my true self and what I really think.

    4. 2.2.3. Ethics Frameworks

      There is another framework that is not mentioned: Feng Shui. It's a traditional Chinese system that focuses on the harmony between the people and their surroundings. Its core focus is on how position and direction can affect a person's mood, health, and fortune. It is believed that creating a balance between the environment and the individual would directly impact the individual's future. My mom is a deep believer in Feng Shui and had to check the Feng Shui for all our homes before letting us live there.

    5. Confucianism

      As I was reading the section about Confucianism it reminded me of another ancient Chinese ethic, *Legalism, which emphasizes adherence to laws and moral order rather than on personal virtue or religious faith. These two ethics represent fundamentally different approaches to governance and morality. When discussing Confucianism, I think it's important to also consider Legalism because the tension and competition between these ethics has shaped Chinese political thought and practice throughout history, particularly in the balance between authority and morality.

    6. There are many more ethics frameworks that we haven’t mentioned here. You can look up some more here.

      I ended up looking at the site that lead to see other ethics frameworks out of curiosity and came across the Social Networking and Ethics page, as I felt it was really relevant to this course. I felt it was really interesting how it talks about separating ethical impacts into direct, indirect, and structural categories. I felt in practice, these categories blur together way more than the framework suggests, like misinformation in social media isn't just a direct harm between users, but it's also shaped by platform algorithms or structure and is accentuated through interactive behavior or indirect behavior. I'm curious if treating these as different categories could oversimplify how responsibility is shared between users like how we discussed during class who is responsible for coding the bluesky bot and running the bot. It seems like harm comes from individual users but in reality, the design of the platform and business models are just as responsible. So instead of how the article defines of thinking of these categories as separate, I think it's more useful to see them together as responsibilities that entail one another so it's easier to see how much power platforms have when certain behaviors are exhibited on the sites.

    7. Utilitarianism: “It is the greatest happiness of the greatest number that is the measure of right and wrong.”

      I find it interesting that utilitarianism is easily the ethical framework I have been most exposed. In modern media and even day-to-day life, we hear variations of the line: "a small sacrifice for the greater good." In fact, many popular villains or anti-heroes use similar reasoning to justify their own pursuits (Thanos, Green Lantern, Magneto, etc). I think what interests me about utilitarianism is that there is rarely a clear definition of "happiness" or "utility" attached. Who gets to decide what happiness looks like?

    1. do we really expect to make a dent on global complexity with a few blunt, local rules

      Kartik is right about the deficiency of most advice to address global complexity and make it legible/coherent. The only thing I've ever come across that does well at managing this is Lawrence Kesteloots dictum to write code top-down.

  3. bafybeif26ootd53sc5vd2gctudzn7odohwsug54lm26ojfjzt743knxotq.ipfs.inbrowser.link bafybeif26ootd53sc5vd2gctudzn7odohwsug54lm26ojfjzt743knxotq.ipfs.inbrowser.link
    1. Origo folder for the Peergos Network Participant named indy

      join this 💬conversation

      Make this link work

      Web design is dreaming up urls as names for morphic capabilities

      use the real advantage of computer languages, which is notational and is amenable to the PUN of exhibiting/invoking desired afforances/functions meta-morphic transformations and interactions

      which them selves are homiconic and autopoietic

      The link does not work yet

      but it eventually be levarages existing capabilities and infrastructures that neede to tREALize its reasonable formulations into REAL interactions

    1. As corporate IT departments have found themselves with long backlogs of requests, Excel users have discovered that they can produce the reports needed to run their businesses themselves using the macro language Visual Basic for Applications (VBA).

      Find macros

    2. VBA enables you to achieve tremendous efficiencies in your day-to-day use of Excel. VBA helps you figure out how to import data and produce reports in Excel so that you don't have to wait for the IT department to help you.

      Find macros

    1. The Central Committee for Palestinian Activism in America is in charge of planning,directing and following up on all work related to and connected to the Group. It includes severalcommittees and organizations, some of which are:- The Islamic Association for Palestine, The Occupied Land Fund, The United Association forStudies & Research, The Ofice of Foreign Affairs, The Investment Committee, TheRehabiIitation Committee, the Medical. Committee and the Legal Committee

      The Occupied Land Fund was renamed the Holy Land Foundation.

  4. bafybeidm5jxlwmwesxujyklfklabmu3a6jqni242x24yqpfgxyrlqd63aa.ipfs.localhost:8080 bafybeidm5jxlwmwesxujyklfklabmu3a6jqni242x24yqpfgxyrlqd63aa.ipfs.localhost:8080
    1. no one has been able to predict what the actual visualization should look like.

      This relates to the prev reading and supports the main argument. The results are not obvious, even if they feel like that after seeing them.

    2. it is not possible to know what a visualization would look like in advance,

      This is an imp point. If we couldn’t predict the result before, then it doesn’t make sense to say it was “obvious” after seeing it!

    3. Knowing that there is a network and knowing what the network is are two different kinds of knowledge.

      Mullen is explaining that general knowledge is not the same as detailed understanding. Someone might know that connections exist, but that does not mean they know how those connections actually look or function. This quote shows that visualizations help turn a vague idea into something specific. It is the difference between guessing something exists and actually seeing how it works.

    1. The group’s political insulation has been such that it also received a share in the Islamic Republic’s confidential 25-year agreement with China.

      Would love to know the details of this agreement!

    2. Sugarcane cultivation in the province consumes close to 4 billion cubic meters of water a year – roughly equivalent to the annual water needs of more than 50 million people. By comparison, total annual urban water consumption in the capital is no more than 1.2 billion cubic meters.

      The amount of waste on this project is staggering - architects of doom!

    3. Iran’s water crisis has deepened to the point where even supplying the capital is becoming difficult, a trajectory shaped in part by Jihad Nasr’s work.

      If it's hard in Tehran, it has to be terrible elsewhere

    4. But Jihad Nasr’s emergence accelerated under President Akbar Hashemi Rafsanjani, and it was further strengthened during successive reformist administrations. Even President Mahmoud Ahmadinejad, despite empowering Khatam al-Anbiya as a counterweight, did not succeed in pushing Jihad Nasr out of the field.

      Amazing the conservatives could not even push this guy out

    1. Often we’ll see tech that is scary. I don’t mean weapons etc. I mean altering video, tech that violates privacy, stuff w obv ethical issues.

      This past weekend, I made all my social media accounts private after a friend told me about Grok AI's ability to sexualize any photo. This level of image generation is among the most frightening aspects of modern internet use. The fact that photos of any girl/woman can be sexualized without their knowledge or consent is horrifying and dangerous. More and more, we are seeing how technology, especially AI, is a weapon, and it is incredibly frightening.

    1. 類似度 0.XXXX: 「Electric vehicles are an environmentally friendly way to travel」 類似度 0.XXXX: 「The weather is sunny today」 類似度 0.XXXX:

      値が全部0.XXXなのでどれが近いかわからない。値を入れて欲しい

    1. Of course one dreads it: of course it is overwhelming: one bothanticipates and fears the kind of swooning, almost erotic pleasure that a good passage in a goodbook gives; as something nameless happens.

      The joy a Literature would bring to our mind.

    2. How can I hope to explain Literature to you, with its capital 'L'? You are bright enough.You could read when you were four. But then, sensibly, you turned to television for your windowon the world: you slaked your appetite for information, for stories, for beginnings, middles andends, with the easy tasty substances of the screen in the living room, and (if I remember yourmother rightly) no doubt in your bedroom too. You lulled yourself to sleep with visions ofviolence, and the cruder strokes of human action and reaction; stories in which every simpleaction has a simple motive, nothing is inexplicable, and even God moves in an un-mysteriousway. And now you realize this is not enough: you have an inkling there is something more, thatyour own feelings and responses are a thousand times more complex than this tinny televisualrepresentation of reality has ever suggested: you have, I suspect and hope, intimations of infinity,of the romance of creation, of the wonder of love, of the glory of existence; you look around forcompanions in your wild new comprehension, your sudden vision, and you see the same zonkedout stares, the same pale faces and dyed cotton-wool hair, and you turn, at last, to education, toliterature, and books – and find them closed to you.

      After a brief introduction, the author goes directly to her point. Literature is more superior to other form of writings.

    Annotators

    1. Compiling resources on how to write within the form of a visual essay while omitting the discussion of storytelling, in both visual and written terms, as well as the importance of story structure, is of next to no help.

      This quote shows that writing alone is not enough when working with data stories. The author is saying that if you only focus on writing and ignore storytelling, the result will not be very useful. It highlights that structure and how you guide the audience matters just as much as the words. It also suggests that data projects are not just technical or informational, they need to be shaped like a story to actually make sense to people.

    1. Neither results seems particularly surprising, once we’ve constructed a just-so story to rationalize the answers.

      This quote shows Lincoln’s main concern really clearly. He is saying that once people have a result in front of them, they are very good at inventing an explanation that makes it feel reasonable. The problem is not just bad interpretation. The problem is that the explanation can sound convincing no matter what the result is. That makes it harder to tell whether the data actually taught us something new, or whether we are just forcing a story onto it after the fact.

    1. Standpoint theorists build on a long tradition of scholarship about the struggle of the oppressed against those with power. In 1807, German philosopher Georg Hegel analyzed master–slave relationships to show that what people “know” about themselves, others, and society depends on which group they are in.8 For example, those in captivity have a decidedly different perspective on the meaning of chains, laws, childbirth, and punishment than do their captors who participate in the same “reality.” But since masters are backed by the established structure of their society, they have the power to make their vie

      Standpoint theorists focus on how people who are oppressed see the world differently from those in power. Georg Hegel studied master, slave relationships to show that what people understand about themselves and society depends on their position. For example, captives experience things like laws, punishment, and childbirth in ways their masters do not. Because those in power control society, they can make their view the “official” one by creating rules and writing history.

    2. “The social groups within which we are located powerfully shape what we experience and know

      Part of this quote points to being the base of the Standpoint theory. Gender, class, and race all play major factors in what we believe, and think is a fact. This theory is proving that objective knowledge does not exist, and how we see the world is based experiences and how it affects us. Where a person came from, and where they stand in society also create different opinions.

    1. How often do you hear phrases like “social media isn’t real life”?

      I often hear the term "social media isn't real life," and honestly, it's something I've said myself. I think it's something that people in my generation (and especially the generation before me) need to be reminded of. Many times, it feels like our world and social lives revolve around social media because it plays such a big role in how we communicate and present ourselves. However, people need to recognize that not everything they see on social media is true. Influencers tend to put on fronts for content and everyday people tend to say things online that they wouldn't say in person because they are hidden behind a screen. If people don't separate social media from reality, it can easily distort their relationships and/or self-image.

    1. By focusing on instructor mindset messages–rather than implementing mindset interventions directed at students–the current research takes an antideficit model of growth mindset interventions (Canning and Limeri, 2023). Focusing exclusively on changing students’ mindset beliefs can potentially ignore contextual effects that present barriers to making this belief system realized (Dweck and Yeager, 2019). An antideficit approach to mindset beliefs considers the institutional, societal, and cultural context that students navigate (Valencia, 2010). The most innovative mindset research takes an antideficit perspective by asking how our institutions and instructors can create environments that allow all students to succeed. The current research is one step to investigate how instructor mindset messages influence students. Nationwide, FG students represent a large pool of potential scientists, engineers, and mathematicians. To provide the most equitable learning environment for these individuals, and to maximize the number of FG students that are retained in scientific fields, it is imperative that we find new and better ways of supporting FG students. Most of the current solutions involve resource-intensive, large-scale institutional transformation that consists of additional advising or freshman seminar courses that teach FG students how to navigate college. In addition to these structural solutions, we propose that by using “wise” intervention techniques, faculty can fairly easily communicate growth mindset messages at critical time points. By providing an adaptive alternative construal–the idea that learning is a process and abilities can improve with effort and effective strategies–FG students may be more supported in college.

      Seems to agree with the conclusions I've come to myself by reading through this article- And it makes me wonder how many of my instructors have read something similar to this!

    2. Finally, although we were able to control for instructor differences through random assignment at the student level, there were some notable differences between our control and intervention materials. First, students may have perceived the growth mindset messages as warmer, friendlier, or more encouraging that the control messages. Indeed, many instructor mindset manipulations in the field confound instructor mindset and instructor demeanor–students tend to perceive growth mindset instructors as warmer and warmer instructors as endorsing growth mindsets (LaCosse et al., 2020; Muenks et al., 2020). Communicating a growth mindset is inherently more encouraging and positive than communicating a fixed mindset message or a neutral message. To address these concerns, we conducted a laboratory experiment in which we separated these constructs (White et al., 2024). We found that the positive effects of an instructors’ growth mindset are not entirely driven by being warm and friendly, as some may have assumed, given how confounded these constructs are in the field. Instead, the growth mindset message had a persistent positive effect on students, even when the delivery was cold or unfriendly. Thus, while the encouraging messages in our intervention may have had a positive effect on students, it is unlikely that positivity alone is the key mechanism of this intervention.

      It seems likely that a mixture of positivity and growth mindset would be the key to maximizing student potentiality.

    3. Additionally, this study was conducted in one course with one instructor. Randomization at the student level (as opposed to randomly assigning entire courses or sections to each condition), allowed us to control for instructor and course characteristics: all students were exposed to the same instructor personality, teaching style, and the instructor’s preexisting mindset beliefs. Although this design allowed for a more controlled experiment, it is unclear whether these effects will generalize to other instructors, disciplines, and course designs. It will be important in future research to test this intervention in courses with instructors that have varying levels of mindset beliefs and teaching styles and within different course structures.

      It seems likely that it would be helpful regardless of teaching style- Unless the instructor already used similar methodology regardless.

    4. While the current study provides some promising strategies that instructors can use in their courses to promote the performance of FG students, it is important to acknowledge its limitations and generalizability. This study was conducted in the spring semester of 2021–a semester with its own unique adaptations and challenges. Due to precautions related to the COVID-19 pandemic, the course was offered online with synchronously delivered Zoom lectures and online proctored exams. In this online learning context, there was no face-to-face communication with the instructor. This environment may have made email communication more salient and potentially could have been more impactful than in a face-to-face course, where students have the chance to chat with the professor after each class period or attend office hours in person. Future research should test various ways to communicate mindset messages (e.g., course redesigns, messages in class, reflection assignments) and should test these techniques in face-to-face classrooms.

      Seems to start to discuss the effects of COVID, I was wondering how it would effect outcomes.

    5. When looking at the timing of the intervention messages, it is important to note that performance effects were only found later in the semester, after students received two doses of the intervention. In our study students were sent two separate email communications: the first was sent immediately after exam 1 and the second was sent immediately after exam 2. If exposure to the intervention message in a single email (i.e., only one dose) was enough to influence immediate performance, we might expect to see a change in exam 2 scores. However, significant effects of the intervention were not detected until the third exam. One interpretation is that two doses of the intervention may be necessary to cement the message in this context. It may also be the case that communication later in the semester is more impactful, when students feel more pressure to improve their performance. It could also be the case that one dose was enough to cause a positive effect, but that the behavioral changes (e.g., study habits or strategies) take more time to compound before improved performance is detected in exam scores. Future research should continue to investigate dosage and timing effects. Multiple growth mindset messages conveyed at different times and with different methods (e.g., emails, syllabus messages, online announcements, messages in class) may have a much larger impact on engagement and performance.

      Makes me wonder how grades could be even further improved, what if the first dose could be given before the first exam? Encouraging growth even if students feel uncomfortable with their potential outcome?

    6. Second, our study provides some preliminary evidence for a behavioral mechanism that supports this recursive cycle. We found that growth mindset messages from the instructor encouraged more course engagement as measured by students’ activity accessing course resources on the course website. Students in the growth mindset condition accessed the course materials on the course website more than students in the control condition, indicating that the growth mindset messages encouraged students to utilize online resources, which in turn increased performance. Interestingly, although there were differences in how often students accessed course materials, there was no significant difference in how frequently students accessed their grades on the course website. In other words, the intervention impacted student engagement with learning materials but did not significantly change student engagement with performance indicators. This suggests that the growth mindset instructor messages may have led students to focus more attention on learning the course material and not on how many points they were earning in the course. When instructors communicate a growth mindset, students are given a pathway for improvement and success. These messages suggest to students that their ability is not defined by a single exam score, but with effort, improved strategies, and seeking-help, ability can improve over time. In turn, students responded to such messages with increased course engagement, which ultimately improved their performance in the course.

      Seems to agree with my previous thoughts, emails encourage students to use online resources more, which in turn helps them to improve their own grade.

    7. How can such a subtle intervention–two emails–have such a large effect on students’ downstream performance outcomes? We hypothesize two reasons. First, we drew from research on wise interventions when developing the content and timing of the intervention messages (Yeager and Walton, 2011; Walton and Wilson, 2018). Wise interventions leverage psychological theory and research to communicate targeted messages (e.g., growth mindset messages from instructors) at critical time points of uncertainty (e.g., directly after exams) to shape how students construe their educational experiences. We hypothesized that delivering growth mindset messages at a time when students may be questioning their ability (i.e., directly after receiving potentially negative performance information on an exam) would provide students with a pathway for subsequent improvements in their biology performance. Wise interventions are theorized to function by initiating a positive recursive cycle that compounds over time. Therefore, even subtle intervention messages can have profound impacts for students when the message is psychologically attuned to the situation and delivered during a time when students may be searching for meaning.

      Unsurprising, most students who fear they received a poor grade would be checking their email like a hawk to see if there is a way to improve their grade, the targeted email gives them this way.

    8. In a large field study, we found that when a biology instructor communicated growth mindset messages at critical times during the semester (i.e., directly after exam grades were posted), students earned higher grades in the course, on average, compared with control messages. However, this effect was most pronounced for FG students. Consistent with other research in higher education, in this course CG students outperformed FG students in the control condition; however, when the instructor communicated a growth mindset belief, the performance difference between CG and FG students was eliminated. This study highlights how instructors’ growth mindset messages can be motivating for FG students, particularly when it comes to academic engagement and performance.

      Was this data what the people performing the research expected?

    9. A test of moderated mediation explored the processes that mediated the effect of instructor growth mindset messages on course performance for FG and CG students. We conducted a moderated mediation analysis (Model 15) using Hayes’ (2018) Process Macro for SPSS and 10000 bootstrapped samples. We tested the indirect effect of instructor growth mindset messages on students’ course performance by accessing the course materials, with FG status as a moderator. The indirect effect was significant for both FG students, indirect effect = 0.027, 95% CI (0.0038, 0.0604), and CG students, indirect effect = 0.031, 95% CI (0.0046, 0.0632). This suggests that instructor growth mindset messages led students to access course materials on the course website, which increased course performance. The index of moderated mediation was not significant, index = –0.004, 95% CI (–0.0252, 0.0159), suggesting that the indirect effect did not differ by generational status.

      Using course materials gives higher grades then not-- Unsurprising.

    10. To understand the behavioral mechanism of the intervention, we examined the two most frequently visited webpages for the course. We found a significant effect of the intervention for the webpage containing course materials, but no significant effect for the webpage containing the gradebook. When the professor communicated a growth mindset (vs. control), all students, on average, had more activity on the webpage containing the course materials, F (1, 410) = 5.123, p = 0.024, ηp2 = 0.012. Compared with students in the control, students who received the growth mindset instructor messages “clicked” on the page containing course materials 40.9 more times during the semester, on average. This represents approximately a 12% increase in webpage engagement across the semester. In contrast, the main effect of condition on course gradebook activity was not significant, F (1, 410) = 1.265, p = 0.261, ηp2 = 0.003, suggesting that there were no differences in the amount of times students accessed their grades. There were no significant condition interactions with FG status for the webpage containing course materials, F (1, 410) = 0.119, p = 0.731, ηp2 = 0.000, or the gradebook, F (1, 410) = 0.130, p = 0.719, ηp2 = 0.000.

      Students who had been given growth mindsets used the given resources more frequently.

    11. Next we examined the effect of the intervention on students’ final course grade. When the professor communicated a growth mindset (vs. control), all students, on average, earned higher grades in the course, F (1, 410) = 4.613, p = 0.032, ηp2 = 0.011. However, this main effect was qualified by an interaction with FG status, F (1, 410) = 3.858, p = 0.050, ηp2 = 0.009 (Figure 2). In the control condition, CG students significantly outperformed FG students, F (1, 410) = 7.018, p = 0.008, earning 0.74 grade points (on a 4.0 scale) higher in the course, on average. However, when the professor communicated a growth mindset, this performance difference was eliminated, F (1, 410) = 0.002, p = 0.962. That is, when the professor communicated growth mindset beliefs (vs. control) it significantly increased FG students’ performance, F (1, 410) = 6.414, p = 0.012, but did not increase CG students’ performance, F (1, 410) = 0.024, p = 0.877.

      So using a growth mindset puts the FG and CG students on a more even playing field, as it were?

    12. We first examined the effect of the intervention on students’ exam scores. The main effect of condition on exam 2 grades was not significant, F (1, 409) = 1.258, p = 0.263, ηp2 = 0.003, and the interaction with FG status was also not significant, F (1, 409) = 1.359, p = 0.244, ηp2 = 0.003. It was not until exam 3, that we found significant effects of the intervention. When the professor communicated a growth mindset (vs. control), all students, on average, earned higher grades on the third exam, F (1, 407) = 3.871, p = 0.050, ηp2 = 0.009. However, this main effect was qualified by an interaction with FG status, F (1, 407) = 3.966, p = 0.047, ηp2 = 0.010 (Figure 1). In the control condition, CG students significantly outperformed FG students, F (1, 407) = 8.086, p = 0.005, earning over a full letter grade higher (10.23 percentage points) on the exam. However, when the professor communicated a growth mindset, there were no differences in exam 3 performance between FG and CG students, F (1, 407) = 0.011, p = 0.917. Examined another way, the effect of the growth mindset messages (vs. control) was significantly larger for FG students (d = 0.37) than it was for CG students (d = 0.02).

      It was not until the third exam that the effects of the intervention became apparent?

    13. In contrast, non-URM students (M = 3.50, SD = 0.52) and URM students (M = 3.43, SD = 0.43) entered the course with roughly equal college GPAs, t (204) = 0.73, p = 0.469. However, by the end of the course, non-URM students earned significantly higher final course grades (M = 2.49, SD = 1.18) than URM students (M = 1.90, SD = 1.16), t (204) = 2.75, p = 0.006, earning over half of a letter grade higher on average (0.59 GPA points). Previous research suggests that URM students might also benefit from instructor growth mindset messaging (Yeager et al., 2022); therefore, in supplementary analyses we also tested the interaction of the intervention with URM status, but these interactions were not significant for any dependent variable (see Table S2 in the Supplemental Materials), suggesting that our results are specific to generational status, not URM status. However, these supplemental results should be interpreted with caution given the small sample of URM students (37 in control; 44 in treatment). The power analysis estimated at least 50 students per condition to detect a small effect size; therefore, these analyses are underpowered. For this reason, we focus the rest of the analyses on FG status.

      Is this due to their higher then average status as a FG, or due to underlying prejudice?

    14. To get a better understanding of the group performance differences within the course, we conducted t tests between CG (n = 137) and FG (n = 69) students, and URM (n = 37) and non-URM (n = 169) students within the control condition. CG students (M = 3.56, SD = 0.46) entered the course with significantly higher college GPAs than FG students (M = 3.34, SD = 0.57), t (204) = 3.11, p = 0.002, entering with almost one-fourth of a letter grade higher on average (0.23 GPA points). At the end of the course, CG students earned significantly higher final course grades (M = 2.63, SD = 1.06) than FG students (M = 1.89, SD = 1.29), t (204) = 4.39, p < 0.001, earning three-fourth of a letter grade higher on average (0.74 GPA points).

      A lot of these results seem to mean nothing to me, as I don't have an understanding of statistical analysis.

    15. To understand the behavioral processes underlying the effects of the growth mindset messages on academic performance, we analyzed students’ activity on the course website, Blackboard. The course website tracks how many times students’ access different pages of the course website. This record contained how many total hits there were for each student for each webpage. Student performance has been positively correlated with higher course website activity (Heffner and Cohen, 2005; Perera and Richardson, 2010; Zhang, 2016; Atherton et al., 2017), and access to online content is frequently used as a measure of course engagement due to the ease of tracking student behaviors and the connection between active accessibility and performance. Importantly, research indicates that merely spending more time on a course website is not correlated with higher performance. Instead, students’ activity or the “clicks” a student makes that are related to specific course resources are correlated with performance (Perera and Richardson, 2010; Atherton et al., 2017). Therefore, we examined the two most frequently visited webpages for this course: the page containing all lecture materials (e.g., PowerPoints, lecture recordings, weekly lecture quizzes), representing 78.56% of all user activity, and the page containing the student’s gradebook, representing 13.0% of all user activity. Together, these two webpages represent more than 90% of all user activity.

      Curious to see how this will relate to FG and CG students. Do CG students tend to use the tools more often?

    16. After the semester was complete, the instructor provided the researchers with students’ exam scores, final course GPA, and records of user activity on the course website, Blackboard. The course contained three midterm exams (100 points each, 15 true/false questions, 34 multiple choice questions). Each exam represented 10% of the student’s final grade in the course. Exams were delivered through the online course website and proctored via webcam observation. We did not examine scores on the first exam because the intervention took place after the first exam scores were released. We examined scores on the second exam (after one dose of the intervention) and the third exam (after both doses of the intervention). We also examined students’ final grade in the course on a 0.0–4.0 scale (A = 4.0, A– = 3.7, B+ = 3.3, B = 3.0, B– = 2.7, C+ = 2.3, C = 2.0, C– = 1.7, D+ = 1.3, D = 1.0, F = 0.0).

      Would students not have had to consent to their grades being viewed by an outside source?

    17. Following best practices for growth mindset field interventions (Yan and Schuetze, 2023), the intervention message also contained five learning strategies framed as strategies that previous students have tried that improved their performance (i.e., study every day after class instead of cramming, study with a group, create concept maps, identify information gaps instead of rereading or rewriting notes, and revisit lecture recordings for unclear material). We chose to include several learning strategies as part of the intervention to indicate that hard work alone will not always result in improvements–it also takes effective strategies and seeking help as needed. Growth mindset messaging that focuses only on effort (e.g., “you just need to try harder”) or positivity (e.g., “you can do anything you set your mind to”) perpetuates a “false growth mindset” and can have deleterious effects for students (Dweck, 2016; Barger et al., 2022). Likewise, giving students learning strategies without a motivational framework, such as growth mindset messaging, is unlikely to change student behavior. Despite most undergraduate students having a relatively sophisticated knowledge of effective learning strategies, students are still unwilling to use them without a motivational framework (Morehead et al., 2016; Rea et al., 2022). We argue that students will be more willing to use effective learning strategies when their instructor communicates that ability is not fixed–it can be improved over time. Thus, the intervention message included the three theory-based growth mindset messages above, in addition to several learning strategies that provide students with a concrete plan to implement those improvements (Yan and Schuetze, 2023). It is important to note that all students were made aware of effective learning strategies during the lecture component of the course; however, only students in the intervention condition received these strategies in the context of growth mindset messaging after the exams. See supplemental material for complete email messages. After the second exam of the semester, each student was sent a second condition-based email to reinforce the manipulation. Thus, each student received either two doses of the intervention message or two doses of the control message.

      "Growth mindset needs to be accompanied by a way to actually improve suggested by the instructor" is what the text appears to be trying to say here?

    18. After the first exam of the semester, each student received a condition-based email from the instructor. In both conditions the email contained the same information about the mean and median exam score, how exam grades were calculated, and that the instructor was happy to meet with them to discuss their grade. The email sent to students in the intervention condition focused on communicating three messages grounded in mindset theory (Dweck, 1999): (1) abilities can be improved (e.g., “I believe that every student, regardless of how well they did on this exam, can improve their skills, learn from their mistakes, and be successful in this course. Remember, learning is a process and often occurs over time….Let me give you a secret to this class–you don’t need to be ‘smart’ to perform at a high level. You can work hard and work effectively to master the material”), (2) academic struggles are normal to experience (e.g., “Here’s how I know this-I have worked with multiple students every semester who performed poorly on Exam 1, but then turned things around and made 30–40 point improvements on their remaining exams”), and (3) academic struggles are the result of controllable rather than uncontrollable factors (e.g., “How did they do it? It wasn’t by suddenly getting a higher IQ. Instead, they figured out better ways to learn in the course. Here’s what they have told me about how they made those kind of improvements…”).1

      If words like these are repeated, is there the possibility that it will begin to seem like meaningless fluff to the students? How can it seem more personally connecting?

    19. All students were randomly assigned to be in the intervention or control condition. Randomization at the student level, as opposed to randomization of sections within the course, allowed us to control for instructor-level characteristics, such as their personality and teaching style, and section-level confounding variables, such as day/time and variation in student characteristics. This design allowed us more statistical power to detect effects and provides a better case for causality than most other educational field intervention studies, which typically use different sections, instructors, or terms as control groups. To randomize students to condition, the research team requested the course roster from the instructor and randomly created different email lists based on condition. The instructor of the course was quasiblind to experimental condition. This means that the instructor was given the email lists to send out the condition-based emails at the appropriate times. However, in a course with over 500 students, it was very unlikely the instructor was able to connect which students were in each condition, even if a student responded to the email. Although unlikely, there is a possibility of observer effects with this design. We risked this possibility, because we wanted the emails to appear as authentic as possible.

      Observer effect: Where people change their behavior because they know that they are being watched.

    20. All students were asked to complete a survey at the beginning of the semester measuring their personal mindset beliefs, their current college GPA, and their demographics. Five items from the Dweck (1999) Theories of Intelligence Scale assessed students’ personal mindset beliefs (e.g., “You have a certain amount of intelligence, and you can’t really do much to change it,” α = 0.85). Students’ current college GPA was obtained based on self-reported answers to the question: “What is your current college GPA?” FG status was determined based on participant’s response to the following question: “What is the highest level of education your primary caregiver has attained?” (Less than high school graduate, High school graduate, Some college/vocational school, Associate’s degree, Bachelor’s degree, Some graduate school, Master’s degree, Law degree, Medical degree, Doctoral degree, Don’t know, Doesn’t apply). This question was also asked in regards to the participant’s secondary caregiver. Students for whom neither parent/guardian obtained a bachelor’s degree or higher were coded as FG college students. Race and ethnicity was determined based on participant’s response to the following question: “What is your race/ethnicity?” (White, Hispanic/Latino, Black/African American, Native American, Pacific Islander, Asian, Multiracial, Other). Students who selected “Multiracial” or “Other” were provided a text box to indicate their identity. Students who selected “Black/African American”, “Hispanic/Latino”, “Native American”, or “Pacific Islander” were coded as underrepresented racial/ethnic minoritized (URM), a common demographic grouping based on historically marginalized groups that are underrepresented in science fields based on the general population (NCSES, 2019). All other students were coded as non-URM. Students were also asked to complete a survey at the end of the semester measuring other variables (see Supplemental Materials); however, only 53% of the sample completed this survey. Thus, the end-of-semester variables were not analyzed given lack of statistical power and disproportional response rates.

      Might the true rate of FG students be higher then indicated, given that this is self reported data, and students might be entirely truthful?

    21. A total of 553 undergraduate students were enrolled in the Introductory Biology course. One hundred sixteen students were excluded from analysis (two students were erroneously excluded from random assignment and were not assigned a condition, 19 students withdrew from the course after random assignment to condition [nine in control condition, 10 in intervention condition], two students received an incomplete in the course, 64 students did not complete the survey at the beginning of the semester, 11 students failed the attention check on the survey, and 18 students were missing one or more covariates), leaving a final sample of 417 students (70.3% female; 34.3% FG; 67.4% White, 8.8% Asian/Asian American, 12.0% Hispanic, 4.8% Black, 2.2% Native American). We conducted a power analysis for an ANCOVA with four groups (2 × 2) using G*Power Version 3.1 (Faul et al., 2007). Estimating 80% statistical power and an α of 0.05, a sample size of at least 200 (or 50 students per group) is needed to detect a small effect size (f2 = 0.2).

      I'll be honest, I'm not entirely sure what a lot of this statistical talk means. I'll likely have to look it up at a later time to gain a higher understanding.

    22. This field experiment took place in a large enrollment Introductory Biology course at a research intensive public university in the Pacific Northwest. We chose an introductory biology course largely because the instructor was willing to collaborate with us and because most introductory biology courses serve as important gateways to persistence in STEM fields (Seymour and Hunter, 2019). This Introductory Biology course is a critical gateway course to further study in the biological sciences. Students typically take this course in their freshmen or sophomore year, and their experiences in this foundational course may determine whether they pursue subsequent coursework in a variety of STEM disciplines. The instructor for this course had 7.5 years of experience teaching the course. The experiment took place during the Spring 2021 semester. This semester was unique in a historical sense in that the course was taught completely online due to COVID-19 precautions. All lectures were delivered synchronously via Zoom and all exams were admini­stered online and proctored by the instructor and graduate teaching assistants.

      Given that this study took place during COVID, I wonder how they interpret that into their data and discuss how it might affect it, as the data was taken during unusual scenario. And might effect the data.

    23. Wise interventions are administered at critical times in a students’ development to elicit recursive processes over time (Yeager and Walton, 2011). We focus on two critical time points in a semester-long course that we hypothesize will have the most impact on students’ performance: immediately after the first two exams. Students often use their exam grades to gauge whether they can be successful in the course (e.g., “Does this score mean that I have what it takes to succeed in this field?”). These time points are especially ripe for an instructor’s growth mindset message to reconstrue the meaning of exam performance and provide a pathway for success (Sato et al., 2018). We hypothesize that when instructors communicate growth mindset (vs. control) language immediately following the posting of exam grades, students will be motivated to engage more with the course material, ultimately earning higher grades in the course, and these messages will be most effective for FG students.

      By encouraging a growth mindset, students are more likely to stay the course and engage more with the course, compared to the higher likelihood of dropping out of a course.

    24. The current research experimentally examines whether growth mindset (vs. control) instructor messages increase performance among FG college students in an introductory biology course. We ground our research hypotheses in organizational mindset theory (Dweck, 1999; Murphy and Dweck, 2010; Canning et al., 2019) and research on “light-touch” or “wise” intervention strategies (Dittmann and Stephens, 2017; Walton and Wilson, 2018; Hammarlund et al., 2022). Wise interventions are designed to promote recursive change through the reconstrual of ambiguous contextual messages, and have been shown to be particularly effective for FG students (Oyserman et al., 2006; Stephens et al., 2014; Paunesku et al., 2015; Stephens et al., 2015; Harackiewicz et al., 2016; Yeager et al., 2016; Browman et al., 2017). This approach can be powerful, because many situations in college are ambiguous and FG students can interpret them in multiple ways. For example, in the absence of intervention, FG students are at risk of construing challenges (e.g., poor exam grades) as a sign that they are not “college material.” Wise interventions disrupt this maladaptive construal and suggest an alternative interpretation (e.g., poor exam grades signal the need to seek additional resources, rather than signal lack of innate ability). This reconstrual can lead to long-term positive outcomes by altering immediate behavior (e.g., accessing course resources), which then supports learning and improved performance in the course.

      So by being upfront that failure does not mean lack of innate ability, it reframes the problem to be a lack of using given resources, rather then a lack of talent?

    25. Recently, a university-wide study conducted with STEM instructors revealed that students earned higher grades when their instructor endorsed more of a growth (vs. fixed) mindset–and this was especially true for stigmatized students (Canning et al., 2019). Further studies illuminate several potential mechanisms. Instructors with growth mindsets engender greater trust, sense of belonging, academic engagement, and fewer feelings of being an imposter among their students (Cavanagh et al., 2018; Rattan et al., 2018; LaCosse et al., 2020; Muenks et al., 2020; Canning et al., 2022; Hecht et al., 2022). In one study, a college instructor built trust with their students in part by communicating a growth mindset, which resulted in students becoming more engaged in the course and earning higher grades (Cavanagh et al., 2018). Another study found that instructor emails containing growth mindset messages increased help-seeking (i.e., attending tutoring sessions) and grades among stigmatized STEM students (Covarrubias et al., 2019). This research suggests that instructor mindset beliefs may be an overlooked barrier and potential point of intervention for FG students, particularly when it comes to academic engagement and performance. Yet, little is known about how instructors can best communicate growth mindset beliefs to students and there is little experimental evidence for specific strategies that instructors can implement in their classes to communicate growth mindset messages and support FG student success.

      Is there the possibility that this is simply due to the students getting more one-on-one attention from their instructors?

    26. Instructors communicate their mindset beliefs through their interactions with students. Instructors who endorse a fixed (vs. growth) mindset are more likely to make quick judgments of students’ ability based on a single-test performance, and are more likely to recommend that struggling students drop difficult courses rather than seek resources that will improve their learning (Rattan et al., 2012). These fixed mindset messages suggest to students that seeking help and spending more time studying the course materials would be futile without inherent ability or talent. However, instructors who promote growth mindset messages can reverse these effects and motivate students to seek additional resources when they struggle (Good et al., 2012; Rattan et al., 2018; Canning et al., 2019).

      I wonder how this information is obtained, I might have to read the cited sources for myself to see how this data was obtained.

    27. While a number of economic and structural factors undoubtedly contribute to the underperformance and attrition of FG students in science fields, these differences may be exacerbated by subtle messages from science instructors that convey the idea that natural talent is necessary to be successful in scientific fields. Indeed, many introductory science courses are designed to “weed out” those students deemed capable and those that are not. Instructor’s mindsets (also known as implicit or lay theories) are their beliefs about the fixedness or malleability of human characteristics like intelligence (Dweck, 1999). Instructors who endorse more of a fixed mindset believe that ability is innate and predetermined–that students either have a particular ability, or they don’t. In contrast, instructors who endorse more of a growth mindset believe that ability is malleable–that it can be developed over time with effort, seeking help, and applying effective learning strategies (Dweck and Leggett, 1988; Dweck, 1999). From the perspective of FG students, instructor mindset beliefs may be salient cues that tell students what kind of person their instructors deem as having the potential to be successful in science fields (i.e., the “innately smart” students vs. the “dedicated or improving” students).

      I wonder why an introductory science course would want to only teach those who already had talent in a subject. Is it a relic of when resources to teach were less, and as a result had to be highly selective of who they taught?

    28. Despite decades of effort to diversify the scientific workforce, first-generation (FG) college students (i.e., those for whom neither parent/guardian obtained a bachelor’s degree) continue to experience worse academic outcomes and persist in science at far lower rates than would be expected based on representation in the college-going population, compared with continuing-generation (CG) students (i.e., students who have at least one parent/guardian who obtained a bachelor’s degree). FG students comprise nearly one-third of all college attendees (Skomsvold, 2015), but they face a number of economic and social obstacles that make succeeding in college more difficult. Compared to CG students, FG students experience more difficulty adapting to college, earn lower course grades, and drop out at higher rates (Terenzini et al., 1996; USDE, 2017; Cataldi et al., 2018). For instance, FG students are less likely to seek help in office hours, ask their instructors to clarify material, or access helpful academic resources, compared with CG students (Kim and Sax, 2009; Calarco, 2014). These group differences are compounded by differences in cultural capital or college “know how”, less familial guidance for navigating higher education, and by the approach, values, and structure of these environments that are not supportive of FG students (Calarco, 2014; Nichols and Islas, 2016; Covarrubias et al., 2020).

      Is the impact of being an FG student lessened by having parents who attended tradeschools or other fields of education?

    29. First-generation (FG) college students (i.e., those for whom neither parent/guardian obtained a bachelor’s degree) experience more barriers in college, compared with continuing-generation students. These barriers are compounded by subtle messages from instructors that convey the idea that natural talent is necessary for success in scientific fields. In contrast, growth mindset messages communicate that ability can improve with effort, help-seeking, and using productive study strategies. In a large enrollment introductory biology course, students were randomly assigned to receive email messages from their instructor after the first two exams containing either a growth mindset or control message. The intervention improved grades in the course for everyone, on average, compared with control messages, and were especially beneficial for FG students. This increase in performance was partially mediated by increased activity accessing course materials on the course website. This study provides preliminary evidence that instructors communicating growth mindset messages can support FG students’ performance.

      While the abstract seemed to focus on how instructors can help FG students, how can CG students help FG students in attaining their education goals?

    1. Hursthouse also emphasizes the importance of what she considers to be new virtues: wonder and respect for nature—two virtues that she finds it necessary to develop for responding adequately to the current biodiversity crisis (Hursthouse, 2007). These virtues we will unfold to a greater extent in Sect. 7.

      I believe what Hursthouse is saying is correct. If you only feel that nature is there to give you something, then you don't have respect for it. When you only care about what something can give you, your acts to help that something in a crisis would be considered less virtuous and disingenuous. When you have wonder and respect for something, your actions to help it in a crisis reflect off of that, proving to be more effective in helping it. This is explained further by the writers of the article, explaining that lacking Hursthouse's new virtues will lead people to overlook moral action and have anthropocentric justifications.

    1. Between January 1-30

      I think the last few years have been a struggle to get this published. I'm fine with leaving it as is, if realistic, however I am thinking everyone might breathe a bit easier if we say by the end of February?

    2. Junior officers: disciplined by CO (Commander+)

      I suspect this could benefit from a touch more clarification, essentally I'm reading this as all Non-CO ship members are Junior Officers disciplined by the CO? Also, can clarify if this is related to highest OOC achieved rank.

    1. Proponents of the schedule claim that it improves school climate and student behavior,

      do teachers find it harder to reign in their students on Mondays coming off a 3 day weekend every week? I feel that sometimes it is hard for those students to come back and comply after a long 3 day weekend, but maybe they become used to it.

    2. Health concerns include increased food insecurity and drug use.

      with 4DSW how do we make sure that our students are being fed? there are situations where when kids come to school breakfast and lunch maybe their only meals they eat that day. So how do we make sure they are fed for 3 days at home instead of just the 2.

    3. In short, while teachers may prefer a four-day schedule, that preference does not appear strong enough on average to outweigh other factors that drive employment decisions, such as salary, leadership quality, workload, and career opportunities.

      Those that have 4DSW, how does that change your workload and salary?

    4. students may also benefit from non-core academic time at school each day, such as morning meetings, recess, passing time between classes, advisory periods, and lunch. And they get less of that time on a 4DSW because they have fewer days of school on average (148 days) than five-day week schools (179 days).

      This I feel is interesting as we have another student teacher at our school and their advisor didn't consider morning meetings beneficial in teaching/learning for the student teacher. There is so much that you can do during morning meeting that are life skill benefits and academic benefits. Life skills being month, year, days of the week, counting by 1's, 5's, and 10's. There is so much that you can do during this morning meeting time that I would hate to see that time different. Even the recess thing, it is so beneficial for our students to get movement and also build relationships and their social skills.

    5. On average, districts add about 50 minutes to each school day.

      How does this affect classified staff? Does their pay still increase and are they still hourly? Or how does their contracts look. Actually even for certified staff do their hours per day get longer beyond the time added to the school days?

    6. research examining four-day schedules’ impacts on students and schools.

      I find this interesting and am curious about because we are a 5 day school week and just within the past couple years 4 day school weeks have been brought up. So interested in finding out more information on this.

    1. In this class, you will be building up a ‘toolbox’ for thinking about ethics.

      I really like this line in this reading because it expresses that the idea of ethics isn't something with just one right answer. I like this framing, but it also made me a little uncomfortable at the same time because if ethics is just a set of tools we choose from, does that mean people can kind of pick the framework that justifies what they already want to do? For example, in social media situations a company could use a consequence based approach on their platform to justify something like data collection and it would benefit people overall and ignore the violation of privacy. Similarly, someone else could use a rights based framework to say the opposite, it almost feels like ethics could be bent and flexible that could be helpful yet also kinda dangerous. In my real life like when I would work in group projects, sometimes people aren't actually disagreeing on the facts, but what "matters more" ethically. The toolbox idea hits the idea well but makes me wonder to what extent do we allow people to use ethics to selectively support their own interests?

    1. bulging

      The use of phrases like "black veins," "slimy rock cod," and "bulging eyes" really hammers home Tan's point about how different/embarrassed she felt. She is definitely emphasizing "weird" aspect of the dinner. It does make you empathize with her and feel bad for her, especially considering that Robert is there.

    2. fourteen

      I like how Tan uses this effective hook to get the reader interested in reading the story. Off the bat, it helps you resonate with her -- she is being open, vulnerable, straight-to-the-point, and relatable. Pretty much everyone has felt this. I think that it is a great way to start.

    1. through politics that went beyond normalizing demands for the inclusion of LGBTQ+ people in existing social institutions.

      activism started as a way to include queerness in mainstream or heteronormative culture, but over time has evolved to resist heteronormative culture in replacement of our own that is built around dismantling current societal structures based on colonialism.

    1. Medical Fraud.

      Is this also why they tested on African Americans for so long? For the cost and due to being able to have test subjects? Doesn't this affect treatment due to different places having different diseases and being immune or more sick to different things? They also did this with birth control early on with poor women who had too many kids and were desperate to not have anymore.

    2. While the explanation for these shortages seems to be multifactorial, a key reason is that the involved drugs are generic, do not make as much money for pharmaceutical companies, and are not prioritized.

      It seems that this whole system is corrupted by the motivation of money. No matter how much you pay in insurance you can still have issues with receiving your medication. How did the Trump administration effect this in 2025 with cutting funding from medicaid?

    3. Pharmaceutical companies have given money to “experts” who then promote their drugs. About 25 percent of biomedical researchers in the United States who study drugs and may speak favorably about them have financial ties to the companies whose products they are studying. This is not illegal, but it is a clear conflict of interest. Studies show that researchers receiving industry funding are 3.5 times more likely to report a result that is favorable to the comp

      I remember when I was younger seeing so many commercials for drugs for depression, anxiety, and other medical issues. It seems like all of those companies have been sued over it. Is this why it has declined so much? I never see the commercials anymore.

    4. The industry justification that the high prices for drugs is necessary to support company research and development is false. The average budget percent for research and development in pharmaceutical companies has been about 15–20 percent. Much of the basic research and development actually occurs in small companies that are then bought out by the large companies, and almost all R&D in the large companies is actually subsidized by the government.

      If this is true then where is all of the money going? Especially with medications for cancer being so high?

    5. cause they make less money for drug manufacturers per dosage, some manufacturers are decreasing their production. This has reduced the competition that has kept the price of generics lower and has led to rapidly escalating prices. Data from 2014 show that if there is only one manufacturer of a generic, it costs 88 percent as much as a brand name. However, if there are nine manufacturers, the cost is only 15 percent as much

      Insurance is likely to only cover generic brands

    6. “Big Pharma”—especially has been subject to much criticism. From 2014 to 2020, already very expensive drug prices rose 33 percent.

      I have read that Big Pharma is responsible for keeping people sick to continue to make money. It is one of the biggest industries in the world.

    7. pay, leaving the poor and uninsured to an overburdened not-for-profit sector, and that physicians’ allegiance to patients would be usurped by their involvement in health-related profit-making ventures

      Is this often why healthcare workers are underpaid? Especially in areas that are public and take insurance. This is also an example of people that fall through the gaps.

    8. Having an increasing number of older people in the population is certainly not unique to the United States. Most countries in the world are experiencing the same trend.

      Is this due to the increasing of life expectancy?

    9. As one nears the end of life, extremely expensive high-technology care is often used to prolong life—sometimes for a matter of only days or weeks, and often in a painful or uncomfortable condition.

      Is this referring to hospice? Is it because it is at a higher rate then normals care would be. What about considering the quality of life?

    1. To be sure, the United States retains a powerful voice in the World Bank and the IMF as well as in the World Trade Organization (WTO), and the leaders of the industrialized “G7” countries meet annually to coordinate global economic policy.

      The United States is A loud voice in world economics

    2. The demise of the Soviet Union and the end of the Cold War was greeted with relief, celebration, and caution: relief for those who had fretted that the Cold War might turn hot; celebration by those committed to the imminent global triumph of the values associated with capitalism and democracy; and caution by those wary of the intent and actions of a United States unburdened by the Soviet threat.

      The end of the Soviet Union wasn’t as messy as they had an anticipated But rather met with celebration

    3. Deep economic distress, political corruption, and weakening control from Moscow emboldened East European citizens to make public demonstrations that the state police and armies suddenly refused to suppress.

      Economic stress was happening globally

    4. when the United States abandoned the gold standard and allowed the value of the dollar to “float,” which it did—down (intentionally, to devalue the dollar and make it possible to pay for oil imports). The U.S. economy was thrown into the horrors of an unexplainable “stagflation,” or economic stagnation and inflation, a combination that baffled economists.

      By America lowering the value of gold and ultimately the value of the dollar it affected many countries economic system

    1. Overall, this reduced metabolite production reflected a transition to a protective, low-activity, quiescent-like state in the early phase after MPN encapsulation.

      MPN encapsulation both protects from stress and alters metabolism. Do you suspect that these are causally linked, or separable? Does metabolic rewiring still occur under non-stress conditions?

    1. Together, these findings establish that SAGA1 and SAGA2 can each compensate for the loss of the other to localize starch sheaths to the pyrenoid, but no other factor can compensate for the loss of both SAGA1 and SAGA2.

      Do you think the redundancy is quantitative (dosage) or qualitative (distinct biochemical roles) (do SAGA1 and SAGA2 differ in binding kinetics, substrate specificity, or temporal expression?). A follow up rescue experiment swapping domains or expression timing could distinguish these.

    2. Our results indicate that whereas SAGA1 is enriched at the starch-tubule-matrix junctions of the pyrenoid, SAGA2 is depleted from these regions, validating previous observations and supporting the idea that SAGA1 and SAGA2 play complementary roles in starch sheath biogenesis

      What do you think defines these spatial domains? Are they driven by intrinsic protein differences (e.g., phase separation propensity, binding affinities) or by pre-existing pyrenoid substructure (e.g., Rubisco density gradients, thylakoid tubules)? Maybe quantitative co-localization with known pyrenoid subcompartments or perturbation of pyrenoid architecture could clarify causality. Correlative light em microscopy or spatial proteomics might help resolve whether these domains correspond to structural features (e.g., thylakoid proximity) or represent emergent patterning driven by SAGA proteins themselves (though i acknowledge these are way beyond the scope of this current study)

    1. I truly enjoy my classroom—it’s a unique, comfortable, student-friendly place to learn.

      I'd say this is probably every teacher and students dream classroom. The calmness and enjoyment students must feel when entering that classroom is beautiful.

    2. Students are unique—and so are groups of students. Some years, my students have needed a lot of structure and little to no freedom. Other years, my students have been able to have total freedom. For flexible seating to work, teachers have to know their students and what they can and can’t handle. So at the beginning of the year, I take time to get to know my students, and I have more of a say in where they sit. As they come to understand the different seating options, I give them more freedom. On occasion, students don’t work well with one type of seating, and I give them limited access to that type. Strong classroom management is key to this model.

      I love this paragraph and agree 100%

    3. Each month, students help me select their new work spots. In addition to the work spots, students have a row spot (for whole group instruction), circle spot (for morning meeting), math spot, reading spot, pod spot (for collaborative times like science experiments), and a test spot (where all students have a flat surface for testing). We tend to use the work spots the most.

      I love this! such a great idea for all students but those especially with ADHD and active students.

    4. I’d encourage any teacher interested in experimenting with flexible seating to go slow, pick one thing to add to your classroom, and continue to add items slowly. I strongly suggest buying quality over quantity. I typically only purchase items that are plastic and can be wiped down, and I avoid fabric—with the exception of my office chairs—because it’s hard to keep clean.

      also helps with financial burden...to slowly add new seating options...it can be frustrating but the result will be worth it

    5. I started by asking students to help me come up with design ideas and to brainstorm ways to transform our classroom with the furniture we had at the time

      this would be so cool if it was financially possible.

    1. Students will recognize their ownresponsibility to stand up to exclusion,prejudice and injustice.

      This quote really stood out to me because it shifts the role of students from just learning about social justice to actually taking action. I agree that it’s important for students to understand that they have a responsibility to speak up, not just adults. At the same time, I think teachers play a big role in modeling and guiding this behavior, especially for younger students who may not feel confident yet. Creating a safe classroom environment can help students build the courage and skills they need to stand up against injustice in real-life situations.

    1. what students could read, in terms of its complexity, was at least as important as what they could do with what theyread.

      This line really stands out because it challenges a common teaching focus on just skills like finding the main idea or making inferences. It emphasizes that text difficulty itself matters just as much as comprehension skills. For me as a future teacher, this means I need to carefully choose texts that push students’ reading levels, not just give them easy texts with harder questions. If students never practice with complex texts, they won’t be prepared for real-world reading in college or careers.

    1. For a radically disruptivetechnology like AI, the human costs must be quantified at a local leveland a global level and carefully weighed against the benefits.

      quantification of human cost doesn't sound like it obeys the Law of VVV Human-Nature.

    2. esthetic response of the image thus becomes decou-pled from the original sources of such aesthetics

      ...but it's VVV human, only natural, to couple two 'somethings' into 'this' thing ... what if we just call the relationship between the responsive feeling and the computationally generated (mysterious) stimuli, an artifact of "Cosmic Love"

      😘

    3. great value that human mathematicians16 gain from visual, kinesthetic,and other sensory intuition, or from intuition grounded by our famil-iarity with the laws of physics, economics, biology, etc.

      may interdisciplinary strangeness remain valued

    4. There are also uncer-tainties in how precedence and credit are assigned. AI-assisted researchalso presents new ethical and legal ramifications and as-yet unansweredquestions on the intellectual property rights of AI-generated content(including proofs)

      release it. Creative Commons

    5. softer” aspects of mathematicalreasoning, such as heuristics and motivation for pursing a result orselecting a proof strategy for that result, experimental evidence11 infavor of (or against) the result, or the trial-and-error process leadingto the discovery of a working argument.

      cute. im here for this

    6. establishing confidence were established for these typesof arguments, such as providing replicable code, isolating the compu-tational components of an argument in specific, clearly stated lemmasseparate from the more conceptual aspects of a paper, and providingadditional related data and “checksums” to check that the computer-generated calculations agree with various “sanity checks”

      model and mechanize, win the game of Mathematics

    7. heuristic, empirical, or metamathematical reason-ing around this core which provides valuable information on why theargument works, whether it extends to other contexts, what the moti-vation is for pursuing these questions, and how one might reconstructthe argument from more basic principles.

      "In mathematics, there is no ignorabimus. We must know; we shall know."(D. Hilbert). and fringe folklore is fun!

    8. generate so many of the traditional markers of quality inthe subject indicates that we have to re-evaluate our models of whatintelligence or creativity actually is, and how it is to be measured

      the Law of VVV Human-Nature is intrinsically immeasurable, those who dare to induct it into computation are foolish

    9. Would the definitions, values,and objectives of such disciplines as mathematics and the humanitiesneed to be re-evaluated?

      there will be no should would could. we'll know it when we feel it. It will be ineffably formative (~10,000 names).

    10. deliberate actions of humans

      spectrum of deliberation is the integer value assigned to 'fuel' parameter when the function is called. resolving states of high entropy require strictly proportional amounts of 'computation'.

      may the meta-function 'wisdom' predict the optimal entropy/fuel solution, always and forever

    1. The variance is the average of the squared deviations.

      I think, it is not definition of Variance, it is how to calculate it. Variance in statistics measures how far a set of numbers are spread out from their mean value. It provides a key measure of data volatility and consistency A high variance indicates data points are widely scattered, while a low variance shows they are clustered closely around the mean.

    2. The standard deviation is a measure of dispersion that can be interpreted as approximately the average distance of each data value from the mean.

      I am not sure the average is correct here I say SD is measurement of Spread which represents the typical distance of data points from the mean.

    1. Below are the same results as above, but for each species. So the data has been grouped by species.

      It took me a little while to understand what this table + plot was showing, a note saying that it's the sum for all species would make it clearer ("grouped" by species could mean different things)

      The first plot is a bar chart, not a histogram (as it isn't measuring counts and the bars aren't touching)

      And what is the first plot measuring? I understand the boxplot to be the average % of days detected per-species. In the boxplot, Far Eastern Curlew have a mean of ~85%, but in the bar chart detection % is very low.

    1. The epistemological question is subtly different. It does not imagine a fallibly justified belief — before asking, without making any actual or hypothetical commitment as to the belief’s truth, whether the belief is knowledge. Rather, the epistemologist’s question considers the conceptual combination of the belief plus the justification for it plus the belief’s being true — which is to say, the whole package that, in this case, is deemed by the Justified-True-Belief

      Epistemology is the study of knowledge including what are the necessary and sufficient conditions for it, it is not the pursuit of knowledge, itself. That is a slightly different area of inquiry.

    1. To support nav-igation of long documents on touchscreen devices, we intro-duce content-aware kinetic scrolling, a novel scrolling tech-nique that dynamically applies pseudo-haptic feedback in theform of friction around points of high interest within the page.
    1. We defined a region in the phasor plot that corresponded exclusively to mCherry labeled voxels. This region was selected manually based on the strain TMR17, which contained only mCherry-labeled histone H2B.

      Hi, really enjoyed this preprint -- we also have a STELLARIS 8 in the lab and are looking forward to trying out your napari plugin on our own data. Thanks for developing and open-sourcing it!

      Quick question about the lifetime unmixing workflow: here you mention manually selecting an ROI on the phasor plot to define the mCherry lifetime signature, then applying that fixed selection across datasets. I'm curious what your thoughts are on automating that selection step, e.g. with some kind of clustering or fitting approach directly on the phasor plot. Do you think that's feasible, or are there practical reasons it needs a human in the loop? I could see how maybe the clusters are too noisy or context-dependent to reliably pick out automatically. I ask because it'd be great to see this plugged into more hands-off analysis pipelines, but I could also see arguments for why manual curation is important here. Would love to hear your take either way!

    1. Example 20.4.5: Finding the Median Using a Frequency Distribution In Example 20.4.2, we looked at a frequency table showing the number of siblings of people who attended a conflict resolution class. Let’s review that data again: what is the median number of siblings?

      Should mention of cumulative frequency before ask finding the median using a F.D.

    2. Mode In our discussion of average heights, the first possible definition we offered was the height that more people share than any other. This is called the mode, the value that appears most often. If there are two modes, the data are said to be bimodal.

      Should mention when there is no mode., in case students do not pay attention to most frequency

    1. all instances of the number “8”appear to have been manually deleted from datesand times in the use-of-force data, requiring imputa-tion to remedy; and (3) civilian ethnicity was excludedfrom stop data despite evidence that HPD tracks thisinformation for its annual reports

      Scary for other reasons

    2. In the division with the lowest share ofRepublican residents, only 2% of civilians are Repub-lican, compared to 37% of officers

      This is more across the board than they made it sound

    3. agency level. For officer race and gender, we rely onagency responses to federal surveys, avoiding the esti-mated voter file proxies. In our behavioral analysis ofChicago and Houston, we use voter file measures ofparty identification but rely on individual-level racialdata obtained through open-records requests.

      No problems

    4. police officers are notonly more likely to affiliate with the Republican Party,they also have higher household income, vote moreoften, and are more likely to be White.

      And when you read this, you're scared

    5. And how do officers with differ-ing partisan affiliations behave when interacting withthose civilians?

      Are the police just going to take matters into their own hands

    6. Our results suggest that despite Republicans’ preference formore punitive law enforcement policy and their overrepresentation in polic-ing, partisan divisions often do not translate into detectable differences inon-the-ground enforcement.

      Thats a nice conclusion

    1. You can use /dev/null in place of the old archive to produce a patch against an empty container. This works everywhere and does not require the special file /dev/null to actually exist or make sense in your operating system.